Image-based phenotyping of plant disease symptoms.
Image-based phenotyping of plant disease symptoms.
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DOI：
10.3389/fpls.2014.00734
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发表时间：
2014
影响因子：
5.6
通讯作者：
Bart RS
中科院分区：
文献类型：
--
作者：
Mutka AM;Bart RS
植物病害导致全世界农业生产力的显著下降。疾病症状对作物的生长和发育具有有害影响，限制产量并使农产品不适合消费。对于许多植物病原体系统，我们缺乏知识的生理机制，连接病原体感染和疾病症状的生产在主机。目前正在开发多种用于植物生长和发育表型分析的基于图像的定量高通量方法。这些方法的范围从随着时间的推移对单个植物的详细分析到对田间数千株植物的作物冠层的广泛评估，并采用各种各样的成像技术。应用这些方法研究植物病害提供了定量研究病原体感染如何改变宿主生理的能力。这些方法有可能提供深入了解疾病症状发展的生理机制。此外，检测可见光之外电磁频谱的成像技术使我们能够量化肉眼不可见的疾病症状，增加我们可以观察到的症状范围，并可能允许更早、更彻底的症状检测。本文综述了植物病害表型研究的最新进展，并提出了今后的发展方向，以加快作物抗病品种的开发。
Plant diseases cause significant reductions in agricultural productivity worldwide. Disease symptoms have deleterious effects on the growth and development of crop plants, limiting yields and making agricultural products unfit for consumption. For many plant–pathogen systems, we lack knowledge of the physiological mechanisms that link pathogen infection and the production of disease symptoms in the host. A variety of quantitative high-throughput image-based methods for phenotyping plant growth and development are currently being developed. These methods range from detailed analysis of a single plant over time to broad assessment of the crop canopy for thousands of plants in a field and employ a wide variety of imaging technologies. Application of these methods to the study of plant disease offers the ability to study quantitatively how host physiology is altered by pathogen infection. These approaches have the potential to provide insight into the physiological mechanisms underlying disease symptom development. Furthermore, imaging techniques that detect the electromagnetic spectrum outside of visible light allow us to quantify disease symptoms that are not visible by eye, increasing the range of symptoms we can observe and potentially allowing for earlier and more thorough symptom detection. In this review, we summarize current progress in plant disease phenotyping and suggest future directions that will accelerate the development of resistant crop varieties.
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DOI：
10.1126/science.1236011
发表时间：
2013-08-16
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Dangl JL;Horvath DM;Staskawicz BJ
通讯作者：
Staskawicz BJ
影响因子：
3
作者：
Berkman, Paul J.;Lai, Kaitao;Edwards, David
通讯作者：
Edwards, David
影响因子：
4.5
作者：
Bock, C. H.;Parker, P. E.;Gottwald, T. R.
通讯作者：
Gottwald, T. R.
影响因子：
7.3
作者：
Chaerle, L;De Boever, F;Van Der Straeten, D
通讯作者：
Van Der Straeten, D
HTPHENO：高通量植物表型的图像分析管道。
影响因子：
3
作者：
Hartmann A;Czauderna T;Hoffmann R;Stein N;Schreiber F
通讯作者：
Schreiber F